Abstract
In this study, a methodology combining some state-of-the-art methods for the analysis of groundwater monitoring networks is proposed to test the hypothesis that it can be successfully used to assess trends in groundwater levels and their origin with minimal hydrogeological data requirements. The methodology first employs trend detection methods, namely the trend-free prewhitening Mann–Kendall test and a bootstrap test, to explore trends; then it uses the reference hydrograph method, land-use change maps, and consultation with groundwater resource managers to understand the origin of the trends. The methodology was tested on the groundwater monitoring network of the province of Quebec, Canada. This study focuses on short-term trends, given the length of the data available. The results showed that all but one of the observation wells in the monitoring network exhibited significant upward (38%) and downward (62%) trends at the 5% statistical significance level, but that the majority of observation wells (77%) exhibited a trend amplitude of less than 3 cm/year in absolute value, the threshold below which the rate of upward or downward change is considered stable. Application of the bootstrap test validated the representativeness of the trends calculated at well scale. For 33% of the observation wells with moderate to large trends (amplitude greater than or equal to 3 cm/year in absolute value), changes in the field around the wells were identified and could explain the observed trends for half of them. For the remaining 67% of wells, no changes in the field were reported.
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Data availability
Groundwater level time series are publicly available on the Quebec Ministry of the Environment website at: https://www.environnement.gouv.qc.ca/eau/piezo/index.htm. The observation wells used in this article are listed in the Excel file in the following directory (Data section), along with the Python codes: https://doi.org/10.5281/zenodo.7933551.
References
Adombi AVDP (2023) Groundwater monitoring network analysis tool. https://doi.org/10.5281/zenodo.7933551
Akakuru OC, Akudinobi B, Opara AI, Onyekuru SO, Akakuru OU (2021) Hydrogeochemical facies and pollution status of groundwater resources of Owerri and environs, Southeastern Nigeria. Environ Monit Assess 193(10):623. https://doi.org/10.1007/s10661-021-09364-9
Awadh SM, Al-Mimar H, Yaseen ZM (2021) Groundwater availability and water demand sustainability over the upper mega aquifers of Arabian Peninsula and west region of Iraq. Environ Dev Sustain 23:1–21. https://doi.org/10.1007/s10668-019-00578-z
Banadkooki FB, Ehteram M, Ahmed AN, Teo FY, Fai CM, Afan HA, Sapitang M, El-Shafie A (2020) Enhancement of groundwater-level prediction using an integrated machine learning model optimized by Whale Algorithm. Nat Resour Res 29(5):3233–3252. https://doi.org/10.1007/s11053-020-09634-2
Bergeron O (2016) Guide d’utilisation 2016 - Grilles climatiques quotidiennes du Programme de surveillance du climat du Québec. version 1.2, Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques, Direction du suivi de l’état de l’environnement, Québec. http://collections.banq.qc.ca/ark:/52327/bs2545297
Bhat S, Motz LH, Pathak C, Kuebler L (2014) Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA. Environ Monit Assess 187(1):4183. https://doi.org/10.1007/s10661-014-4183-x
Bikše J, Retike I, Haaf E, Kalvāns A (2023) Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns. J Hydrol 620:129424. https://doi.org/10.1016/j.jhydrol.2023.129424
Caloiero T, Coscarelli R, Ferrari E (2018) Application of the innovative trend analysis method for the trend analysis of rainfall anomalies in Southern Italy. Water Resour Manag 32(15):4971–4983. https://doi.org/10.1007/s11269-018-2117-z
Collenteur RA, Bakker M, Klammler G, Birk S (2021) Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data. Hydrol Earth Syst Sci 25(5):2931–2949. https://hess.copernicus.org/articles/25/2931/2021/
Environmental Reporting BC (2019) Long-term trends in groundwater levels in B.C. https://www.env.gov.bc.ca/soe/indicators/water/groundwater-levels.html
Famiglietti JS (2014) The global groundwater crisis. Nat Clim Change 4(11):945–948. https://doi.org/10.1038/nclimate2425
Farrell RP, Whiteman M (2023) The Environment Agency Chalk groundwater level monitoring network in England. Geological Society, London, Special Publications, vol 517, no 1, pp 163–182. https://www.lyellcollection.org/doi/abs/10.1144/SP517-2022-274
Gagné S, Larocque M, Pinti DL, Saby M, Meyzonnat G, Méjean P (2018) Benefits and limitations of using isotope-derived groundwater travel times and major ion chemistry to validate a regional groundwater flow model: example from the Centre-du-Québec region, Canada. Can Water Resour J Revue Canadienne Des Ressources Hydriques 43(2):195–213. https://doi.org/10.1080/07011784.2017.1394801
Gebremicael TG, Mohamed YA, Betrie GD, van der Zaag P, Teferi E (2013) Trend analysis of runoff and sediment fluxes in the Upper Blue Nile basin: a combined analysis of statistical tests, physically-based models and landuse maps. J Hydrol 482:57–68. https://doi.org/10.1016/j.jhydrol.2012.12.023
Green TR, Taniguchi M, Kooi H, Gurdak JJ, Allen DM, Hiscock KM, Treidel H, Aureli A (2011) Beneath the surface of global change: impacts of climate change on groundwater. J Hydrol 405(3):532–560. https://doi.org/10.1016/j.jhydrol.2011.05.002
Halder S, Roy MB, Roy PK (2020) Analysis of groundwater level trend and groundwater drought using Standard Groundwater Level Index: a case study of an eastern river basin of West Bengal, India. SN Appl Sci 2(3):507. https://doi.org/10.1007/s42452-020-2302-6
Hussain M, Mahmud I (2019) pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J Open Source Softw 4(39):1556
Kroes J, van Dam J, Supit I, de Abelleyra D, Verón S, de Wit A, Boogaard H, Angelini M, Damiano F, Groenendijk P, Wesseling J, Veldhuizen A (2019) Agrohydrological analysis of groundwater recharge and land use changes in the Pampas of Argentina. Agric Water Manag 213:843–857. https://doi.org/10.1016/j.agwat.2018.12.008
Larocque M, Cloutier V, Levison J, Rosa E (2018) Results from the Quebec Groundwater Knowledge Acquisition Program. Can Water Resour J Revue Canadienne Des Ressources Hydriques 43(2):69–74. https://doi.org/10.1080/07011784.2018.1472040
Lee J-Y, Yi M-J, Yoo Y-K, Ahn K-H, Kim G-B, Won J-H (2007) A review of the National Groundwater Monitoring Network in Korea. Hydrol Process 21(7):907–919. https://doi.org/10.1002/hyp.6282
Lehr C, Lischeid G (2020) Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors. Hydrol Earth Syst Sci 24(2):501–513. https://hess.copernicus.org/articles/24/501/2020/
Marchant BP, Cuba D, Brauns B, Bloomfield JP (2022) Temporal interpolation of groundwater level hydrographs for regional drought analysis using mixed models. Hydrogeol J 30(6):1801–1817. https://doi.org/10.1007/s10040-022-02528-y
Meggiorin M, Passadore G, Bertoldo S, Sottani A, Rinaldo A (2023) Comparison of three imputation methods for groundwater level timeseries. Water 15(4):801. https://doi.org/10.3390/w15040801
MELCCFP (2023a) Normales climatiques 1981–2010. https://www.environnement.gouv.qc.ca/climat/normales/climat-qc.htm
MELCCFP (2023b) Programme d’acquisition de connaissances sur les eaux souterraines. http://www.mddelcc.gouv.qc.ca/eau/souterraines/programmes/acquisition-connaissance.htm
Nath B, Ni-Meister W, Choudhury R (2021) Impact of urbanization on land use and land cover change in Guwahati city, India and its implication on declining groundwater level. Groundw Sustain Dev 12:100500. https://doi.org/10.1016/j.gsd.2020.100500
Oudin L, Hervieu F, Michel C, Perrin C, Andréassian V, Anctil F, Loumagne C (2005) Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 2—towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. J Hydrol 303(1):290–306. https://doi.org/10.1016/j.jhydrol.2004.08.026
Öztopal A, Şen Z (2017) Innovative trend methodology applications to precipitation records in Turkey. Water Resour Manag 31(3):727–737. https://doi.org/10.1007/s11269-016-1343-5
Pathak AA, Dodamani BM (2019) Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha River Basin, India. Nat Resour Res 28(3):631–643. https://doi.org/10.1007/s11053-018-9417-0
Patle GT, Singh DK, Sarangi A, Rai A, Khanna M, Sahoo RN (2015) Time series analysis of groundwater levels and projection of future trend. J Geol Soc India 85(2):232–242. https://doi.org/10.1007/s12594-015-0209-4
Qureshi AS, Gill MA, Sarwar A (2010) Sustainable groundwater management in Pakistan: challenges and opportunities. Irrig Drain 59(2):107–116. https://doi.org/10.1002/ird.455
Rey N, Rosa E, Cloutier V, Lefebvre R (2018) Using water stable isotopes for tracing surface and groundwater flow systems in the Barlow-Ojibway Clay Belt, Quebec, Canada. Can Water Resour J Revue Canadienne Des Ressources Hydriques 43(2):173–194. https://doi.org/10.1080/07011784.2017.1403960
Rivard C, Vigneault H, Piggott AR, Larocque M, Anctil F (2009) Groundwater recharge trends in Canada. Can J Earth Sci 46(11):841–854. https://doi.org/10.1139/E09-056
Rivera A (2014) Canada’s groundwater resources. Fitzhenry & Whiteside, Leaside
Sahoo S, Russo TA, Elliott J, Foster I (2017) Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resour Res 53(5):3878–3895. https://doi.org/10.1002/2016WR019933
Satish Kumar K, Venkata Rathnam E (2019) Analysis and prediction of groundwater level trends using four variations of Mann Kendall tests and ARIMA modelling. J Geol Soc India 94(3):281–289. https://doi.org/10.1007/s12594-019-1308-4
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63(324):1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556
Shamsudduha M, Chandler RE, Taylor RG, Ahmed KM (2009) Recent trends in groundwater levels in a highly seasonal hydrological system: the Ganges-Brahmaputra-Meghna Delta. Hydrol Earth Syst Sci 13(12):2373–2385. https://doi.org/10.5194/hess-13-2373-2009
Sonali P, Nagesh Kumar D (2013) Review of trend detection methods and their application to detect temperature changes in India. J Hydrol 476:212–227. https://doi.org/10.1016/j.jhydrol.2012.10.034
Thomas EA, Needoba J, Kaberia D, Butterworth J, Adams EC, Oduor P, Macharia D, Mitheu F, Mugo R, Nagel C (2019) Quantifying increased groundwater demand from prolonged drought in the East African Rift Valley. Sci Total Environ 666:1265–1272. https://doi.org/10.1016/j.scitotenv.2019.02.206
Valéry A (2010) Modélisation précipitations débit sous influence nivale: Elaboration d'un module neige et évaluation sur 380 bassins versants. Doctorat Hydrobiologie, Institut des Sciences et Industries du Vivant et de l'Environnement AgroParisTech, p 417
Valéry A, Andréassian V, Perrin C (2014) ‘As simple as possible but not simpler’: what is useful in a temperature-based snow-accounting routine? Part 2—sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments. J Hydrol 517:1176–1187. https://doi.org/10.1016/j.jhydrol.2014.04.058
Vu MT, Jardani A, Massei N, Fournier M (2021) Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network. J Hydrol 597:125776. https://doi.org/10.1016/j.jhydrol.2020.125776
Wada Y, van Beek LPH, van Kempen CM, Reckman JWTM, Vasak S, Bierkens MFP (2010) Global depletion of groundwater resources. Geophys Res Lett. https://doi.org/10.1029/2010GL044571
Walter J, Rouleau A, Chesnaux R, Lambert M, Daigneault R (2018) Characterization of general and singular features of major aquifer systems in the Saguenay-Lac-Saint-Jean region. Can Water Res J Revue Canadienne Des Ressources Hydriques 43(2):75–91. https://doi.org/10.1080/07011784.2018.1433069
Wanda E, Monjerezi M, Mwatseteza JF, Kazembe LN (2011) Hydro-geochemical appraisal of groundwater quality from weathered basement aquifers in Northern Malawi. Phys Chem Earth Parts a/b/c 36(14):1197–1207. https://doi.org/10.1016/j.pce.2011.07.061
Wu Y (2004) Optimal design of a groundwater monitoring network in Daqing, China. Environ Geol 45(4):527–535. https://doi.org/10.1007/s00254-003-0907-x
Yesertener C (2005) Impacts of climate, land and water use on declining groundwater levels in the Gnangara Groundwater Mound, Perth Australia. Australas J Water Resour 8(2):143–152. https://doi.org/10.1080/13241583.2005.11465251
Yira Y, Diekkrüger B, Steup G, Bossa AY (2016) Modeling land use change impacts on water resources in a tropical West African catchment (Dano, Burkina Faso). J Hydrol 537:187–199. https://doi.org/10.1016/j.jhydrol.2016.03.052
Yue S, Wang CY (2002) Applicability of prewhitening to eliminate the influence of serial correlation on the Mann–Kendall test. Water Resour Res 38(6):4-1–4-7. https://doi.org/10.1029/2001WR000861
Yue S, Pilon P, Cavadias G (2002a) Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259(1):254–271. https://doi.org/10.1016/S0022-1694(01)00594-7
Yue S, Pilon P, Phinney B, Cavadias G (2002b) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16(9):1807–1829. https://doi.org/10.1002/hyp.1095
Yue S, Pilon P, Phinney BOB (2003) Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrol Sci J 48(1):51–63. https://doi.org/10.1623/hysj.48.1.51.43478
Zakaria N, Anornu G, Adomako D, Owusu-Nimo F, Gibrilla A (2021) Evolution of groundwater hydrogeochemistry and assessment of groundwater quality in the Anayari catchment. Groundw Sustain Dev 12:100489. https://doi.org/10.1016/j.gsd.2020.100489
Acknowledgements
The authors would like to thank the Quebec Ministry of the Environment for the data provided.
Funding
The authors acknowledge the financial support of the Natural Science and Engineering Research Council (NSERC-federal funding) of Canada in the framework of the Individual Discovery Grant Program held by Prof. Romain Chesnaux. The financial support of Fondation de l’Université du Québec à Chicoutimi (FUQAC), Rio Tinto Graduate Scholarships Program, as well as Fonds de Recherche du Québec – Nature et Technologie (FRQNT-provincial funding) are also acknowledged.
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AVDP Adombi: conceptualization, methodology, data curation, visualization, validation, writing—original draft preparation, and investigation. Romain Chesnaux: conceptualization, supervision, resources, reviewing and editing. Marie-Amélie Boucher: conceptualization, supervision, reviewing and editing.
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Adombi, A.V., Chesnaux, R. & Boucher, MA. Toward a methodology to explore historical groundwater level trends and their origin: the case of Quebec, Canada. Environ Earth Sci 83, 183 (2024). https://doi.org/10.1007/s12665-024-11466-9
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DOI: https://doi.org/10.1007/s12665-024-11466-9